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Problem and objective
Large Language Models (LLMs) are experienc-
ing a significant surge in popularity. Following
the release of ChatGPT, their adoption has dra-
matically increased, with a broad range of users
reporting widespread usage and practical ben-
efits. By leveraging Generative Artificial Intelli-
gence (GenAI) in work environments, productiv-
ity can be enhanced, and results improved.
While most tasks utilize LLMs‘ extensive gen-
eral knowledge, these models can be fine-tuned
to understand and solve complex problems. Ex-
amples include safety monitoring during pro-
duction or generative design for improved ma-
terial usage. Therefore, LLMs and GenAI have
the potential to revolutionize entire industries.
LLMs can generate multiple types of content,
such as images, text, or source code. A notable
example of the utilization of LLMs for source
code generation is the GitHub Copilot, which
significantly impacted the field of software de-
velopment.
Source code generation can also be used to
control industrial robots, like ABBs YuMi series.
Currently, programming such robots requires
expert knowledge of the robot’s mechanics and
a translation of the desired movements into
code. This creates a barrier to using robots due
to high training and implementation costs. Con-
trolling the robot via natural language would sig-
nificantly lower this barrier. However, it remains
unclear whether and how LLMs can effectively
solve this problem.
To address this uncertainty, the main objective
of this thesis is to develop a concept for robot
control using LLMs to translate natural lan-
guage inputs into suitable code for the robot.
This includes a comprehensive analysis of ex-
isting LLM-based approaches for source code
generation, evaluating the capabilities of LLMs
for robot control, developing a concept for robot
control using LLMs, designing a reference ar-
chitecture, and implementing a proof of con-
cept. Furthermore, the developed approach will
be generalized and abstracted into a generic
methodology that can be reused for future pro-
jects.
Work program
1. Familiarization with the task and
preparation of a detailed outline
130 h
2. Problem analysis to determine re-
quirements for the approach
150 h
3. Analysis of the state of the art 120 h
5. Development and validation of a
systematic approach
• Identification of relevant
communication interfaces
• Selection of suitable training
data
• Tuning of the selected LLM
• Implementation and valida-
tion of the approach
200 h
6. Documentation of the results 130 h
7. Final thesis presentation 20 h
750 h
Remarks
The times planned in the work program are
standard values.
Supervision
M.Sc. Benjamin Tiggemann
M.Sc. Ruslan Bernijazov
Heinz Nixdorf Institut
Universität Paderborn
Fürstenallee 11, 33102 Paderborn
E-Mail: Benjamin.Tiggemann@hni.upb.de
E-Mail: Ruslan.Bernijazov@hni.upb.de
Um sich für diesen Job zu bewerben, sende deine Unterlagen per E-Mail an Benjamin.Tiggemann@hni.upb.de